Implementation of an ontology for syndromic classification of animal health data
In an era of ubiquitous electronic collection of data, there is growing opportunity to monitor the health of populations in real-time. However, hindrances to integration and interoperability among systems using different data sources remain a barrier. An ontology is a tool to model domain knowledge. It allows those in the domain to share a common vocabulary that is understandable both by humans and machines.
With funding from Vinnova we have, over the last 3 years, established a collaboration of health providers, surveillance experts and ontology experts to develop the Animal Health Surveillance Ontology (AHSO). The ontology is maintained in an open access repository (GitHub).
During the next phase we will deploy the ontology in the everyday work of disease surveillance performed by the National Veterinary Institute (SVA). The ontology will be used to improve and further automate the process of extracting information relevant for health surveillance from raw medical data. That is, data such as laboratory or clinical data will be processed faster, and with higher sensitivity when looking for trends in the occurrence of animals diseases; interoperability will be reached with systems from other health providers using similar data (for example pathology data across health providers); and it will be possible to combine evidence from different data sources (for example clinical and laboratory data being evaluated for early disease detection at the population level).